Autoregressive (AR) models have been shown to be appropriate for time-series modelling in various fields
such as econometric [1]} and climate forecasting [2]}. In condition monitoring,
it is generally used for fault detection by establishing an AR model using healthy conditions under various loads
and using this model on the observed data recorded in in-service conditions in order to trend the residual signals.
Such approaches does not make use of analytical description of faults or collection of typical fault
patterns [3]}, [4]}.
Such a method has been used for gear monitoring by [5]}, and [6]} used a similar approach together with a logit model to determine the probability of failure of an elevator door motion system.
[7]} suggested to use a pole representation of an AR process to detect bearings faults.
[8]} compared ARIMA (AR with integrated moving average) with two other models for battery prognostics.
